Key Concepts and Terminology of Artificial Intelligence
This course provides a high-level overview of artificial intelligence, including the differences between AI, machine learning, and deep learning. Participants will learn fundamental terms and concepts, such as neural networks, supervised and unsupervised learning, and model training.
Why It’s Worth It
Gain a foundational understanding of AI, ML, and DL, which is essential for any tech-related career.
Improve your problem-solving skills by learning how to apply AI concepts to real-world scenarios.
Stay competitive in the job market with in-demand knowledge of cutting-edge AI technologies.
Your Learning Roadmap
Introduction to Artificial Intelligence
This module introduces the basic history, definitions, and key distinctions between artificial intelligence, machine learning, and deep learning. Learners will gain an appreciation for the evolution of AI and its real-world applications. The module emphasizes conceptual clarity and contextual background, which is essential for grasping more specialized content later in the course. History of AI Defining AI, ML, and DL AI Applications Overview
Core Concepts and Terminology
This module delves into essential terminology and fundamental concepts that serve as the building blocks of AI. Participants will explore key ideas such as data representation, algorithms, and learning paradigms. The module connects theoretical frameworks from popular texts to everyday language and applications in artificial intelligence. Basic Terminology in AI Understanding Algorithms and Models Supervised vs. Unsupervised Learning
Neural Networks and Deep Learning Foundations
This module focuses on the architecture and functionality of neural networks, a central area of deep learning. Participants will explore how deep learning structures mirror aspects of human cognition. The lessons build on core AI concepts and connect to established frameworks discussed in popular texts on deep learning. Fundamentals of Neural Networks Neural Network Architecture Activation Functions & Learning
Machine Learning Paradigms
This module examines different paradigms in machine learning including supervised, unsupervised, and reinforcement learning. Participants will learn how each paradigm addresses specific types of problems. The module builds on earlier defined concepts and provides insights to effectively choose methods for different practical AI tasks. Supervised Learning Explained The World of Unsupervised Learning Introduction to Reinforcement Learning
Model Training, Evaluation and Deployment
This module covers essential procedures and best practices related to building, training, and assessing AI models. It explains how to optimize model performance through hyperparameter tuning and evaluation metrics. The lessons integrate theoretical understanding with practical considerations for deploying AI systems in real-world scenarios. Model Training Processes Hyperparameter Tuning and Evaluation Deployment Considerations
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All You Need to Know
Join the AI Revolution!
Instant feedback through AI chat support
Engaging real-time interactions with your tutor
Flexible learning schedule tailored to you
Access to a comprehensive glossary of AI terms
Real-world examples connecting theory to practice
Step-by-step guidance through complex concepts